Possibilities and limitations of statistical regression models for the calculation of threshold values for minimum provider volumes;Möglichkeiten und grenzen statistischer regressionsmodelle zur berechnung von schwellenwerten für mindestmengen

Zeitschrift fur Arztliche Fortbildung und Qualitatssicherung

Volumn 100, Issue 2, 2006, Pages 93-98

  Bender, Ralf ^a   Grouven, Ulrich ^a  

^a INSTITUTE FOR QUALITY AND EFFICIENCY IN HEALTH CARE IQWIG   (Germany)

Author keywords

Confidence intervals; Hospitals; Logistic models; Outcome assessment (health care); Quality indicators; Threshold limit values

Indexed keywords

CALCULATION; CONFIDENCE INTERVAL; HEALTH CARE DELIVERY; HOSPITAL SERVICE; MEDICAL RESEARCH; OUTCOME ASSESSMENT; PHYSICIAN; REGRESSION ANALYSIS; REVIEW; RISK ASSESSMENT; SIMULATION; STATISTICAL MODEL;

DATA INTERPRETATION, STATISTICAL; GERMANY; HEALTH SERVICES; HEALTH SERVICES RESEARCH; HOSPITALS; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); REGRESSION ANALYSIS;

EID: 33645938599     PISSN: 14317621     EISSN: 16180992     Source Type: Journal    
DOI: None     Document Type: Review

References (19)

1. Rathmann W, Windeler J. Zusammenhang zwischen Behandiungsmenge und Behandlungsqualität. Evidenzbericht, Fachbereich Evidenz-basierte Medizin des MDS, Essen, 2002.
2. Geraedts M. Evidenz zur Ableitung von Mindestmengen in der Medizin. Gutachten im Auftrag der Bundesärztekammer, 2003.
3. Wetzet H. Überlegungen zum möglichen methodischen Vorgehen für die Definition und Evaluation von Mindestmengen. Z ärztl Fortbild Qual Gesundh wes 2006;10099-106.
4. Bender R, Ziegler A, Lange S. Logistische Regression. Dtsch Med Wochenschr 2002;127:T11-T18.
5. Dim K. A statistical method for assessing a threshold in epidemiological studies. Stat Med 1991;10:341-349.
6. Küchenhoff H. An exact algorithm for estimating breakpoints in segmented generalized linear models. Comput Stat 1997;12:235-247.
7. Diabetes Control and Complications Trial Research Group. The absence of a glycemic threshold for the development of long-term complications: The perspective of the Diabetes Control and Complications Trial. Diabetes 1996;45:1289-1298.
8. Bender R. Quantitative risk assessment in epidemiological studies investigating threshold effects. Biom J 1999;41:305-319.
9. Sawicki PT, Bender R, Berger M, Mühlhauser I. Non-linear effects of blood pressure and glycosylated haemoglobin on progression of diabetic nephropathy. J Intern Med 2000;247:131-138.
10. Budtz-Jørgensen E, Keiding N, Grandjean P. Benchmark dose calculation from epidemiological data. Biometrics 2001;57:698-706.
11. Crump KS. A new method for determining allowable daily intake. Fundam Appl Toxicol 1984;4:854-871.
12. Jones RH, Molitoris BA. A statistical method for determining the breakpoint of two lines. Anal Biochem 1984;141:287-290.
13. Crump KS. Calculations of benchmark doses from continuous data. Risk Anal 1995;15:79-89.
14. Molinari N, Daurès J-P, Durand J-F. Regression splines for threshold selection in survival data analysis. Stat Med 2001;20:237-247.
15. Küchenhoff H, Carroll RJ. Segmented regression with errors in predictors: Semi-parametric and parametric methods. Stat Med 1997;16:169-188.
16. Benedetti A, Abrahamowicz M, Goldberg M, Tamblyn R, Leffondré K. Using generalized additive models to detect and estimate thresholds in the association between two continuous variables. Biom J 2006 (submitted).
17. Mohr VD, Bauer J, Döbler K, Eckert O, Fischer B, Woldenga C (Hrsg.). Qualität sichtbar machen. BQS-Qualitätsreport 2003. Düsseldorf: BQS Bundesgeschäftsstelle Qualitätssicherung gGmbH, 2004.
18. Panageas KS, Schrag D, Riedel E, Bach PB, Begg CB. The effect of clustering of outcomes on the association of procedure volume and surgical outcomes. Ann Intern Med 2003;139:658-665.
19. Urbach DR, Austin PC. Conventional models overestimate the statistical significance of volume-outcome associations, compared with multilevel models. J Clin Epidemiol 2005;58:391-400.